Overview

Brought to you by YData

Dataset statistics

Number of variables113
Number of observations621
Missing cells49680
Missing cells (%)70.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory535.6 KiB
Average record size in memory883.2 B

Variable types

Categorical18
Text1
Numeric11
Unsupported80
Boolean3

Alerts

Engine has constant value "vllm"Constant
Model has constant value "meta-llama/Meta-Llama-3.1-8B-Instruct"Constant
model_name has constant value "meta-llama/Meta-Llama-3.1-8B-Instruct"Constant
tensor_parallel_size has constant value "2"Constant
pipeline_parallel_size has constant value "1"Constant
kv_cache_dtype has constant value "auto"Constant
gpu_memory_utilization has constant value "0.9"Constant
swap_space has constant value "4"Constant
cpu_kv_cache_size has constant value "40"Constant
block_size has constant value "8"Constant
enable_prefix_cache has constant value "False"Constant
disable_sliding_window has constant value "False"Constant
enforce_eager has constant value "False"Constant
max_seq_len_to_capture has constant value "8192"Constant
attention_backend has constant value "TORCH_SDPA"Constant
attn_input_reshape_min has 621 (100.0%) missing valuesMissing
attn_input_reshape_max has 621 (100.0%) missing valuesMissing
attn_input_reshape_mean has 621 (100.0%) missing valuesMissing
attn_input_reshape_median has 621 (100.0%) missing valuesMissing
attn_input_reshape_std has 621 (100.0%) missing valuesMissing
attn_kv_cache_save_min has 621 (100.0%) missing valuesMissing
attn_kv_cache_save_max has 621 (100.0%) missing valuesMissing
attn_kv_cache_save_mean has 621 (100.0%) missing valuesMissing
attn_kv_cache_save_median has 621 (100.0%) missing valuesMissing
attn_kv_cache_save_std has 621 (100.0%) missing valuesMissing
attn_prefill_min has 621 (100.0%) missing valuesMissing
attn_prefill_max has 621 (100.0%) missing valuesMissing
attn_prefill_mean has 621 (100.0%) missing valuesMissing
attn_prefill_median has 621 (100.0%) missing valuesMissing
attn_prefill_std has 621 (100.0%) missing valuesMissing
attn_decode_min has 621 (100.0%) missing valuesMissing
attn_decode_max has 621 (100.0%) missing valuesMissing
attn_decode_mean has 621 (100.0%) missing valuesMissing
attn_decode_median has 621 (100.0%) missing valuesMissing
attn_decode_std has 621 (100.0%) missing valuesMissing
attn_output_reshape_min has 621 (100.0%) missing valuesMissing
attn_output_reshape_max has 621 (100.0%) missing valuesMissing
attn_output_reshape_mean has 621 (100.0%) missing valuesMissing
attn_output_reshape_median has 621 (100.0%) missing valuesMissing
attn_output_reshape_std has 621 (100.0%) missing valuesMissing
embed_min has 621 (100.0%) missing valuesMissing
embed_max has 621 (100.0%) missing valuesMissing
embed_mean has 621 (100.0%) missing valuesMissing
embed_median has 621 (100.0%) missing valuesMissing
embed_std has 621 (100.0%) missing valuesMissing
input_layernorm_min has 621 (100.0%) missing valuesMissing
input_layernorm_max has 621 (100.0%) missing valuesMissing
input_layernorm_mean has 621 (100.0%) missing valuesMissing
input_layernorm_median has 621 (100.0%) missing valuesMissing
input_layernorm_std has 621 (100.0%) missing valuesMissing
attn_pre_proj_min has 621 (100.0%) missing valuesMissing
attn_pre_proj_max has 621 (100.0%) missing valuesMissing
attn_pre_proj_mean has 621 (100.0%) missing valuesMissing
attn_pre_proj_median has 621 (100.0%) missing valuesMissing
attn_pre_proj_std has 621 (100.0%) missing valuesMissing
attn_rope_min has 621 (100.0%) missing valuesMissing
attn_rope_max has 621 (100.0%) missing valuesMissing
attn_rope_mean has 621 (100.0%) missing valuesMissing
attn_rope_median has 621 (100.0%) missing valuesMissing
attn_rope_std has 621 (100.0%) missing valuesMissing
attn_post_proj_min has 621 (100.0%) missing valuesMissing
attn_post_proj_max has 621 (100.0%) missing valuesMissing
attn_post_proj_mean has 621 (100.0%) missing valuesMissing
attn_post_proj_median has 621 (100.0%) missing valuesMissing
attn_post_proj_std has 621 (100.0%) missing valuesMissing
post_attention_layernorm_min has 621 (100.0%) missing valuesMissing
post_attention_layernorm_max has 621 (100.0%) missing valuesMissing
post_attention_layernorm_mean has 621 (100.0%) missing valuesMissing
post_attention_layernorm_median has 621 (100.0%) missing valuesMissing
post_attention_layernorm_std has 621 (100.0%) missing valuesMissing
mlp_up_proj_min has 621 (100.0%) missing valuesMissing
mlp_up_proj_max has 621 (100.0%) missing valuesMissing
mlp_up_proj_mean has 621 (100.0%) missing valuesMissing
mlp_up_proj_median has 621 (100.0%) missing valuesMissing
mlp_up_proj_std has 621 (100.0%) missing valuesMissing
mlp_act_min has 621 (100.0%) missing valuesMissing
mlp_act_max has 621 (100.0%) missing valuesMissing
mlp_act_mean has 621 (100.0%) missing valuesMissing
mlp_act_median has 621 (100.0%) missing valuesMissing
mlp_act_std has 621 (100.0%) missing valuesMissing
mlp_down_proj_min has 621 (100.0%) missing valuesMissing
mlp_down_proj_max has 621 (100.0%) missing valuesMissing
mlp_down_proj_mean has 621 (100.0%) missing valuesMissing
mlp_down_proj_median has 621 (100.0%) missing valuesMissing
mlp_down_proj_std has 621 (100.0%) missing valuesMissing
mlp_add_min has 621 (100.0%) missing valuesMissing
mlp_add_max has 621 (100.0%) missing valuesMissing
mlp_add_mean has 621 (100.0%) missing valuesMissing
mlp_add_median has 621 (100.0%) missing valuesMissing
mlp_add_std has 621 (100.0%) missing valuesMissing
distributed_backend has 621 (100.0%) missing valuesMissing
max_num_batched_tokens has 621 (100.0%) missing valuesMissing
enable_chunked_prefill has 621 (100.0%) missing valuesMissing
quantization has 621 (100.0%) missing valuesMissing
rope_scaling has 621 (100.0%) missing valuesMissing
run_id has unique valuesUnique
Mean TTFT (ms) has unique valuesUnique
attn_input_reshape_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_input_reshape_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_input_reshape_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_input_reshape_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_input_reshape_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_kv_cache_save_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_kv_cache_save_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_kv_cache_save_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_kv_cache_save_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_kv_cache_save_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_prefill_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_prefill_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_prefill_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_prefill_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_prefill_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_decode_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_decode_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_decode_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_decode_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_decode_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_output_reshape_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_output_reshape_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_output_reshape_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_output_reshape_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_output_reshape_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
embed_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
embed_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
embed_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
embed_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
embed_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
input_layernorm_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
input_layernorm_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
input_layernorm_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
input_layernorm_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
input_layernorm_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_pre_proj_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_pre_proj_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_pre_proj_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_pre_proj_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_pre_proj_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_rope_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_rope_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_rope_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_rope_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_rope_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_post_proj_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_post_proj_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_post_proj_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_post_proj_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
attn_post_proj_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
post_attention_layernorm_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
post_attention_layernorm_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
post_attention_layernorm_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
post_attention_layernorm_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
post_attention_layernorm_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_up_proj_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_up_proj_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_up_proj_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_up_proj_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_up_proj_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_act_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_act_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_act_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_act_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_act_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_down_proj_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_down_proj_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_down_proj_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_down_proj_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_down_proj_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_add_min is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_add_max is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_add_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_add_median is an unsupported type, check if it needs cleaning or further analysisUnsupported
mlp_add_std is an unsupported type, check if it needs cleaning or further analysisUnsupported
distributed_backend is an unsupported type, check if it needs cleaning or further analysisUnsupported
max_num_batched_tokens is an unsupported type, check if it needs cleaning or further analysisUnsupported
enable_chunked_prefill is an unsupported type, check if it needs cleaning or further analysisUnsupported
quantization is an unsupported type, check if it needs cleaning or further analysisUnsupported
rope_scaling is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-09-28 08:18:12.453109
Analysis finished2024-09-28 08:18:35.732404
Duration23.28 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Engine
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
vllm
621 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2484
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvllm
2nd rowvllm
3rd rowvllm
4th rowvllm
5th rowvllm

Common Values

ValueCountFrequency (%)
vllm 621
100.0%

Length

2024-09-28T08:18:35.844137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:35.977770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
vllm 621
100.0%

Most occurring characters

ValueCountFrequency (%)
l 1242
50.0%
v 621
25.0%
m 621
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1242
50.0%
v 621
25.0%
m 621
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1242
50.0%
v 621
25.0%
m 621
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1242
50.0%
v 621
25.0%
m 621
25.0%

engine_config_id
Categorical

Distinct12
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
c82767ea
63 
7c22728c
63 
e9d9877a
63 
bf298285
63 
6f28cde9
63 
Other values (7)
306 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4968
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowc82767ea
2nd rowc82767ea
3rd rowc82767ea
4th rowc82767ea
5th rowc82767ea

Common Values

ValueCountFrequency (%)
c82767ea 63
10.1%
7c22728c 63
10.1%
e9d9877a 63
10.1%
bf298285 63
10.1%
6f28cde9 63
10.1%
f6f9bcf8 63
10.1%
35918276 63
10.1%
b1e23d38 63
10.1%
4e74f945 59
9.5%
bad28275 20
 
3.2%
Other values (2) 38
6.1%

Length

2024-09-28T08:18:36.132610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c82767ea 63
10.1%
7c22728c 63
10.1%
e9d9877a 63
10.1%
bf298285 63
10.1%
6f28cde9 63
10.1%
f6f9bcf8 63
10.1%
35918276 63
10.1%
b1e23d38 63
10.1%
4e74f945 59
9.5%
bad28275 20
 
3.2%
Other values (2) 38
6.1%

Most occurring characters

ValueCountFrequency (%)
2 626
12.6%
8 606
12.2%
7 558
11.2%
9 475
9.6%
f 374
7.5%
c 334
 
6.7%
e 311
 
6.3%
d 266
 
5.4%
6 252
 
5.1%
5 243
 
4.9%
Other values (5) 923
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 626
12.6%
8 606
12.2%
7 558
11.2%
9 475
9.6%
f 374
7.5%
c 334
 
6.7%
e 311
 
6.3%
d 266
 
5.4%
6 252
 
5.1%
5 243
 
4.9%
Other values (5) 923
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 626
12.6%
8 606
12.2%
7 558
11.2%
9 475
9.6%
f 374
7.5%
c 334
 
6.7%
e 311
 
6.3%
d 266
 
5.4%
6 252
 
5.1%
5 243
 
4.9%
Other values (5) 923
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 626
12.6%
8 606
12.2%
7 558
11.2%
9 475
9.6%
f 374
7.5%
c 334
 
6.7%
e 311
 
6.3%
d 266
 
5.4%
6 252
 
5.1%
5 243
 
4.9%
Other values (5) 923
18.6%

run_id
Text

UNIQUE 

Distinct621
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:36.507607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4968
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique621 ?
Unique (%)100.0%

Sample

1st rowe67282eb
2nd row7d6bf0cd
3rd rowd5a06a09
4th rowd450ced3
5th row935cadeb
ValueCountFrequency (%)
aaea0149 1
 
0.2%
044b3dde 1
 
0.2%
e67282eb 1
 
0.2%
7d6bf0cd 1
 
0.2%
d5a06a09 1
 
0.2%
d450ced3 1
 
0.2%
935cadeb 1
 
0.2%
c2ef6f38 1
 
0.2%
88b77240 1
 
0.2%
c2f59fc1 1
 
0.2%
Other values (611) 611
98.4%
2024-09-28T08:18:37.062008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 348
 
7.0%
a 337
 
6.8%
b 324
 
6.5%
0 324
 
6.5%
e 323
 
6.5%
d 315
 
6.3%
8 308
 
6.2%
9 307
 
6.2%
4 306
 
6.2%
3 306
 
6.2%
Other values (6) 1770
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 348
 
7.0%
a 337
 
6.8%
b 324
 
6.5%
0 324
 
6.5%
e 323
 
6.5%
d 315
 
6.3%
8 308
 
6.2%
9 307
 
6.2%
4 306
 
6.2%
3 306
 
6.2%
Other values (6) 1770
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 348
 
7.0%
a 337
 
6.8%
b 324
 
6.5%
0 324
 
6.5%
e 323
 
6.5%
d 315
 
6.3%
8 308
 
6.2%
9 307
 
6.2%
4 306
 
6.2%
3 306
 
6.2%
Other values (6) 1770
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 348
 
7.0%
a 337
 
6.8%
b 324
 
6.5%
0 324
 
6.5%
e 323
 
6.5%
d 315
 
6.3%
8 308
 
6.2%
9 307
 
6.2%
4 306
 
6.2%
3 306
 
6.2%
Other values (6) 1770
35.6%

Model
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
meta-llama/Meta-Llama-3.1-8B-Instruct
621 

Length

Max length37
Median length37
Mean length37
Min length37

Characters and Unicode

Total characters22977
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmeta-llama/Meta-Llama-3.1-8B-Instruct
2nd rowmeta-llama/Meta-Llama-3.1-8B-Instruct
3rd rowmeta-llama/Meta-Llama-3.1-8B-Instruct
4th rowmeta-llama/Meta-Llama-3.1-8B-Instruct
5th rowmeta-llama/Meta-Llama-3.1-8B-Instruct

Common Values

ValueCountFrequency (%)
meta-llama/Meta-Llama-3.1-8B-Instruct 621
100.0%

Length

2024-09-28T08:18:37.251606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:37.401186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
meta-llama/meta-llama-3.1-8b-instruct 621
100.0%

Most occurring characters

ValueCountFrequency (%)
a 3726
16.2%
- 3105
13.5%
t 2484
 
10.8%
m 1863
 
8.1%
l 1863
 
8.1%
e 1242
 
5.4%
/ 621
 
2.7%
M 621
 
2.7%
L 621
 
2.7%
3 621
 
2.7%
Other values (10) 6210
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3726
16.2%
- 3105
13.5%
t 2484
 
10.8%
m 1863
 
8.1%
l 1863
 
8.1%
e 1242
 
5.4%
/ 621
 
2.7%
M 621
 
2.7%
L 621
 
2.7%
3 621
 
2.7%
Other values (10) 6210
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3726
16.2%
- 3105
13.5%
t 2484
 
10.8%
m 1863
 
8.1%
l 1863
 
8.1%
e 1242
 
5.4%
/ 621
 
2.7%
M 621
 
2.7%
L 621
 
2.7%
3 621
 
2.7%
Other values (10) 6210
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3726
16.2%
- 3105
13.5%
t 2484
 
10.8%
m 1863
 
8.1%
l 1863
 
8.1%
e 1242
 
5.4%
/ 621
 
2.7%
M 621
 
2.7%
L 621
 
2.7%
3 621
 
2.7%
Other values (10) 6210
27.0%
Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
50
247 
200
189 
550
185 

Length

Max length3
Median length3
Mean length2.6022544
Min length2

Characters and Unicode

Total characters1616
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row50

Common Values

ValueCountFrequency (%)
50 247
39.8%
200 189
30.4%
550 185
29.8%

Length

2024-09-28T08:18:37.563214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:37.715473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
50 247
39.8%
200 189
30.4%
550 185
29.8%

Most occurring characters

ValueCountFrequency (%)
0 810
50.1%
5 617
38.2%
2 189
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 810
50.1%
5 617
38.2%
2 189
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 810
50.1%
5 617
38.2%
2 189
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 810
50.1%
5 617
38.2%
2 189
 
11.7%
Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
128
210 
250
210 
550
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1863
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row128
2nd row128
3rd row128
4th row128
5th row128

Common Values

ValueCountFrequency (%)
128 210
33.8%
250 210
33.8%
550 201
32.4%

Length

2024-09-28T08:18:37.882296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:38.025477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
128 210
33.8%
250 210
33.8%
550 201
32.4%

Most occurring characters

ValueCountFrequency (%)
5 612
32.9%
2 420
22.5%
0 411
22.1%
1 210
 
11.3%
8 210
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 612
32.9%
2 420
22.5%
0 411
22.1%
1 210
 
11.3%
8 210
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 612
32.9%
2 420
22.5%
0 411
22.1%
1 210
 
11.3%
8 210
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 612
32.9%
2 420
22.5%
0 411
22.1%
1 210
 
11.3%
8 210
 
11.3%

Concurrent Requests
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.31401
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:38.174050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median30
Q375
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)65

Descriptive statistics

Standard deviation33.30707
Coefficient of variation (CV)0.82619095
Kurtosis-0.99590213
Mean40.31401
Median Absolute Deviation (MAD)20
Skewness0.57672409
Sum25035
Variance1109.3609
MonotonicityNot monotonic
2024-09-28T08:18:38.334991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 90
14.5%
10 90
14.5%
20 90
14.5%
30 89
14.3%
50 89
14.3%
75 87
14.0%
100 86
13.8%
ValueCountFrequency (%)
1 90
14.5%
10 90
14.5%
20 90
14.5%
30 89
14.3%
50 89
14.3%
75 87
14.0%
100 86
13.8%
ValueCountFrequency (%)
100 86
13.8%
75 87
14.0%
50 89
14.3%
30 89
14.3%
20 90
14.5%
10 90
14.5%
1 90
14.5%

Completed Requests
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.31401
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:38.494921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median30
Q375
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)65

Descriptive statistics

Standard deviation33.30707
Coefficient of variation (CV)0.82619095
Kurtosis-0.99590213
Mean40.31401
Median Absolute Deviation (MAD)20
Skewness0.57672409
Sum25035
Variance1109.3609
MonotonicityNot monotonic
2024-09-28T08:18:38.651967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 90
14.5%
10 90
14.5%
20 90
14.5%
30 89
14.3%
50 89
14.3%
75 87
14.0%
100 86
13.8%
ValueCountFrequency (%)
1 90
14.5%
10 90
14.5%
20 90
14.5%
30 89
14.3%
50 89
14.3%
75 87
14.0%
100 86
13.8%
ValueCountFrequency (%)
100 86
13.8%
75 87
14.0%
50 89
14.3%
30 89
14.3%
20 90
14.5%
10 90
14.5%
1 90
14.5%

Duration (s)
Real number (ℝ)

Distinct599
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89599
Minimum9.9
Maximum408.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:38.854142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.9
5-th percentile12.19
Q124.7
median46.45
Q385.98
95-th percentile181.91
Maximum408.99
Range399.09
Interquartile range (IQR)61.28

Descriptive statistics

Standard deviation59.166796
Coefficient of variation (CV)0.89788158
Kurtosis5.837341
Mean65.89599
Median Absolute Deviation (MAD)24.47
Skewness2.1214577
Sum40921.41
Variance3500.7097
MonotonicityNot monotonic
2024-09-28T08:18:39.078823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.1 3
 
0.5%
63.31 2
 
0.3%
58.15 2
 
0.3%
23.23 2
 
0.3%
10.27 2
 
0.3%
68.02 2
 
0.3%
13.26 2
 
0.3%
16.28 2
 
0.3%
85.29 2
 
0.3%
9.95 2
 
0.3%
Other values (589) 600
96.6%
ValueCountFrequency (%)
9.9 1
0.2%
9.91 1
0.2%
9.95 2
0.3%
9.97 2
0.3%
9.99 2
0.3%
10.01 1
0.2%
10.05 1
0.2%
10.11 1
0.2%
10.25 1
0.2%
10.27 2
0.3%
ValueCountFrequency (%)
408.99 1
0.2%
401.67 1
0.2%
321.86 1
0.2%
319.48 1
0.2%
303.01 1
0.2%
296.74 1
0.2%
294.12 1
0.2%
270.36 1
0.2%
259.71 1
0.2%
257.09 1
0.2%
Distinct552
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.363527
Minimum0.68
Maximum225.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:39.291958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.68
5-th percentile1.5
Q114.91
median35.05
Q366.41
95-th percentile130.83
Maximum225.81
Range225.13
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation41.999333
Coefficient of variation (CV)0.90587012
Kurtosis2.2972433
Mean46.363527
Median Absolute Deviation (MAD)22.64
Skewness1.4465034
Sum28791.75
Variance1763.944
MonotonicityNot monotonic
2024-09-28T08:18:39.991522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7 7
 
1.1%
1.38 6
 
1.0%
1.37 4
 
0.6%
0.69 4
 
0.6%
1.39 3
 
0.5%
2.97 3
 
0.5%
1.53 3
 
0.5%
3.01 3
 
0.5%
3 3
 
0.5%
2.93 3
 
0.5%
Other values (542) 582
93.7%
ValueCountFrequency (%)
0.68 1
 
0.2%
0.69 4
0.6%
0.7 7
1.1%
1.35 2
 
0.3%
1.36 3
0.5%
1.37 4
0.6%
1.38 6
1.0%
1.39 3
0.5%
1.49 1
 
0.2%
1.5 1
 
0.2%
ValueCountFrequency (%)
225.81 1
0.2%
222.68 1
0.2%
221.4 1
0.2%
208.61 1
0.2%
204.97 1
0.2%
194.15 1
0.2%
177.72 1
0.2%
177.07 1
0.2%
175.27 1
0.2%
175.04 1
0.2%
Distinct595
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.73842
Minimum5.51
Maximum471.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:40.206143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.51
5-th percentile6.35
Q198.31
median162.62
Q3233.06
95-th percentile352.03
Maximum471.88
Range466.37
Interquartile range (IQR)134.75

Descriptive statistics

Standard deviation105.36193
Coefficient of variation (CV)0.63571217
Kurtosis-0.33178811
Mean165.73842
Median Absolute Deviation (MAD)65.27
Skewness0.36761325
Sum102923.56
Variance11101.137
MonotonicityNot monotonic
2024-09-28T08:18:40.407316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.48 5
 
0.8%
6.31 3
 
0.5%
12.26 3
 
0.5%
185.39 2
 
0.3%
6.32 2
 
0.3%
150.88 2
 
0.3%
197.41 2
 
0.3%
110.95 2
 
0.3%
12.54 2
 
0.3%
11.72 2
 
0.3%
Other values (585) 596
96.0%
ValueCountFrequency (%)
5.51 1
0.2%
5.61 1
0.2%
5.62 1
0.2%
5.67 1
0.2%
5.7 2
0.3%
5.76 1
0.2%
5.78 1
0.2%
5.8 1
0.2%
5.81 1
0.2%
6.05 1
0.2%
ValueCountFrequency (%)
471.88 1
0.2%
466.7 1
0.2%
466.03 1
0.2%
463.47 1
0.2%
460.02 1
0.2%
453.06 1
0.2%
435.72 1
0.2%
429.07 1
0.2%
410.38 1
0.2%
405.38 1
0.2%
Distinct439
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3422705
Minimum1.27
Maximum12.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:40.610458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile2.15
Q13.98
median5.7
Q38.71
95-th percentile12.56
Maximum12.93
Range11.66
Interquartile range (IQR)4.73

Descriptive statistics

Standard deviation3.1682055
Coefficient of variation (CV)0.49953805
Kurtosis-0.74005207
Mean6.3422705
Median Absolute Deviation (MAD)2.22
Skewness0.57764697
Sum3938.55
Variance10.037526
MonotonicityNot monotonic
2024-09-28T08:18:40.817900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 4
 
0.6%
3.51 4
 
0.6%
4.33 4
 
0.6%
4.22 4
 
0.6%
5.35 4
 
0.6%
5.88 4
 
0.6%
3.77 3
 
0.5%
3.46 3
 
0.5%
4.78 3
 
0.5%
4.25 3
 
0.5%
Other values (429) 585
94.2%
ValueCountFrequency (%)
1.27 1
0.2%
1.29 1
0.2%
1.34 1
0.2%
1.35 1
0.2%
1.37 1
0.2%
1.4 2
0.3%
1.42 1
0.2%
1.59 1
0.2%
1.64 2
0.3%
1.71 2
0.3%
ValueCountFrequency (%)
12.93 1
0.2%
12.88 1
0.2%
12.87 1
0.2%
12.86 1
0.2%
12.85 1
0.2%
12.84 1
0.2%
12.83 1
0.2%
12.82 1
0.2%
12.81 1
0.2%
12.79 2
0.3%
Distinct600
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.869614
Minimum9.9
Maximum408.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:41.015728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.9
5-th percentile12.1
Q124.26
median44.69
Q385.26
95-th percentile171.95
Maximum408.82
Range398.92
Interquartile range (IQR)61

Descriptive statistics

Standard deviation56.254546
Coefficient of variation (CV)0.89478116
Kurtosis7.252924
Mean62.869614
Median Absolute Deviation (MAD)23.55
Skewness2.2882123
Sum39042.03
Variance3164.5739
MonotonicityNot monotonic
2024-09-28T08:18:41.262680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.29 2
 
0.3%
24.94 2
 
0.3%
9.95 2
 
0.3%
9.99 2
 
0.3%
67.93 2
 
0.3%
21.92 2
 
0.3%
53.08 2
 
0.3%
80.35 2
 
0.3%
10.27 2
 
0.3%
11.63 2
 
0.3%
Other values (590) 601
96.8%
ValueCountFrequency (%)
9.9 1
0.2%
9.91 1
0.2%
9.95 2
0.3%
9.97 2
0.3%
9.99 2
0.3%
10.01 1
0.2%
10.05 1
0.2%
10.11 1
0.2%
10.25 1
0.2%
10.27 2
0.3%
ValueCountFrequency (%)
408.82 1
0.2%
401.5 1
0.2%
321.66 1
0.2%
319.32 1
0.2%
302.89 1
0.2%
296.59 1
0.2%
293.96 1
0.2%
259.56 1
0.2%
256.95 1
0.2%
251.9 1
0.2%

Mean TTFT (ms)
Real number (ℝ)

UNIQUE 

Distinct621
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4927.9603
Minimum118.09
Maximum62123.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:41.476908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum118.09
5-th percentile135.28
Q1908.07
median2272.42
Q35778.2
95-th percentile15993.71
Maximum62123.77
Range62005.68
Interquartile range (IQR)4870.13

Descriptive statistics

Standard deviation7652.3177
Coefficient of variation (CV)1.5528367
Kurtosis18.807584
Mean4927.9603
Median Absolute Deviation (MAD)1797.24
Skewness3.739502
Sum3060263.3
Variance58557967
MonotonicityNot monotonic
2024-09-28T08:18:41.677879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4353.85 1
 
0.2%
134.34 1
 
0.2%
485.02 1
 
0.2%
852.09 1
 
0.2%
1167.88 1
 
0.2%
1493.15 1
 
0.2%
4710.97 1
 
0.2%
8675.49 1
 
0.2%
134.31 1
 
0.2%
483.62 1
 
0.2%
Other values (611) 611
98.4%
ValueCountFrequency (%)
118.09 1
0.2%
120.24 1
0.2%
120.62 1
0.2%
120.68 1
0.2%
120.78 1
0.2%
121.27 1
0.2%
122.07 1
0.2%
122.62 1
0.2%
123.09 1
0.2%
123.72 1
0.2%
ValueCountFrequency (%)
62123.77 1
0.2%
61691.27 1
0.2%
61151.66 1
0.2%
43912.03 1
0.2%
43613.85 1
0.2%
43395.86 1
0.2%
36289.5 1
0.2%
35972.64 1
0.2%
35653.44 1
0.2%
33745.5 1
0.2%

P95 TTFT (ms)
Real number (ℝ)

Distinct620
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11188.78
Minimum118.09
Maximum177512.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:41.880360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum118.09
5-th percentile135.28
Q11079.06
median2777.03
Q37974.5
95-th percentile52134.93
Maximum177512.27
Range177394.18
Interquartile range (IQR)6895.44

Descriptive statistics

Standard deviation24391.3
Coefficient of variation (CV)2.1799786
Kurtosis17.342639
Mean11188.78
Median Absolute Deviation (MAD)2240.18
Skewness3.9360196
Sum6948232.2
Variance5.9493553 × 108
MonotonicityNot monotonic
2024-09-28T08:18:42.097273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3117.08 2
 
0.3%
9782.4 1
 
0.2%
208.34 1
 
0.2%
1184.65 1
 
0.2%
2499.33 1
 
0.2%
3226.16 1
 
0.2%
5521.16 1
 
0.2%
7841.24 1
 
0.2%
10649.7 1
 
0.2%
7695.18 1
 
0.2%
Other values (610) 610
98.2%
ValueCountFrequency (%)
118.09 1
0.2%
120.24 1
0.2%
120.62 1
0.2%
120.68 1
0.2%
120.78 1
0.2%
121.27 1
0.2%
122.07 1
0.2%
122.62 1
0.2%
123.09 1
0.2%
123.72 1
0.2%
ValueCountFrequency (%)
177512.27 1
0.2%
158181.89 1
0.2%
157111.73 1
0.2%
155407.44 1
0.2%
153948.84 1
0.2%
152639.06 1
0.2%
150118.69 1
0.2%
113323.7 1
0.2%
112796.51 1
0.2%
112549.76 1
0.2%
Distinct615
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.73208
Minimum77.36
Maximum784.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:42.313743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum77.36
5-th percentile79.62
Q1114.74
median175.83
Q3252.11
95-th percentile466.02
Maximum784.29
Range706.93
Interquartile range (IQR)137.37

Descriptive statistics

Standard deviation126.70877
Coefficient of variation (CV)0.60127896
Kurtosis3.4637352
Mean210.73208
Median Absolute Deviation (MAD)67.47
Skewness1.6768845
Sum130864.62
Variance16055.111
MonotonicityNot monotonic
2024-09-28T08:18:42.525891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.86 2
 
0.3%
79.1 2
 
0.3%
169.89 2
 
0.3%
167.23 2
 
0.3%
89.26 2
 
0.3%
126.96 2
 
0.3%
105.52 1
 
0.2%
122.57 1
 
0.2%
169.64 1
 
0.2%
227.46 1
 
0.2%
Other values (605) 605
97.4%
ValueCountFrequency (%)
77.36 1
0.2%
77.61 1
0.2%
77.71 1
0.2%
77.75 1
0.2%
77.83 1
0.2%
77.86 1
0.2%
77.92 1
0.2%
78.02 1
0.2%
78.05 1
0.2%
78.16 1
0.2%
ValueCountFrequency (%)
784.29 1
0.2%
774.41 1
0.2%
744.17 1
0.2%
743.47 1
0.2%
729.99 1
0.2%
716.31 1
0.2%
713.06 1
0.2%
704.68 1
0.2%
627.4 1
0.2%
610.76 1
0.2%
Distinct615
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.63081
Minimum77.36
Maximum790.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-09-28T08:18:42.721843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum77.36
5-th percentile79.62
Q1118.85
median184.29
Q3286.4
95-th percentile506.83
Maximum790.09
Range712.73
Interquartile range (IQR)167.55

Descriptive statistics

Standard deviation138.56042
Coefficient of variation (CV)0.61139272
Kurtosis1.7847596
Mean226.63081
Median Absolute Deviation (MAD)76.97
Skewness1.3684936
Sum140737.73
Variance19198.991
MonotonicityNot monotonic
2024-09-28T08:18:42.953444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
227.55 2
 
0.3%
97.98 2
 
0.3%
79.1 2
 
0.3%
160.94 2
 
0.3%
152.43 2
 
0.3%
78.86 2
 
0.3%
170.7 1
 
0.2%
78.05 1
 
0.2%
228.88 1
 
0.2%
290.54 1
 
0.2%
Other values (605) 605
97.4%
ValueCountFrequency (%)
77.36 1
0.2%
77.61 1
0.2%
77.71 1
0.2%
77.75 1
0.2%
77.83 1
0.2%
77.86 1
0.2%
77.92 1
0.2%
78.02 1
0.2%
78.05 1
0.2%
78.16 1
0.2%
ValueCountFrequency (%)
790.09 1
0.2%
779.42 1
0.2%
749.51 1
0.2%
746.09 1
0.2%
732.47 1
0.2%
721.06 1
0.2%
715.95 1
0.2%
707.32 1
0.2%
631.24 1
0.2%
616.16 1
0.2%

attn_input_reshape_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_input_reshape_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_input_reshape_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_input_reshape_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_input_reshape_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_kv_cache_save_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_kv_cache_save_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_kv_cache_save_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_kv_cache_save_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_kv_cache_save_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_prefill_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_prefill_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_prefill_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_prefill_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_prefill_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_decode_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_decode_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_decode_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_decode_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_decode_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_output_reshape_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_output_reshape_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_output_reshape_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_output_reshape_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_output_reshape_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

embed_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

embed_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

embed_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

embed_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

embed_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

input_layernorm_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

input_layernorm_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

input_layernorm_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

input_layernorm_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

input_layernorm_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_pre_proj_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_pre_proj_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_pre_proj_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_pre_proj_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_pre_proj_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_rope_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_rope_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_rope_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_rope_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_rope_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_post_proj_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_post_proj_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_post_proj_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_post_proj_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

attn_post_proj_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

post_attention_layernorm_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

post_attention_layernorm_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

post_attention_layernorm_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

post_attention_layernorm_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

post_attention_layernorm_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_up_proj_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_up_proj_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_up_proj_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_up_proj_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_up_proj_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_act_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_act_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_act_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_act_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_act_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_down_proj_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_down_proj_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_down_proj_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_down_proj_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_down_proj_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_add_min
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_add_max
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_add_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_add_median
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

mlp_add_std
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

model_name
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
meta-llama/Meta-Llama-3.1-8B-Instruct
621 

Length

Max length37
Median length37
Mean length37
Min length37

Characters and Unicode

Total characters22977
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmeta-llama/Meta-Llama-3.1-8B-Instruct
2nd rowmeta-llama/Meta-Llama-3.1-8B-Instruct
3rd rowmeta-llama/Meta-Llama-3.1-8B-Instruct
4th rowmeta-llama/Meta-Llama-3.1-8B-Instruct
5th rowmeta-llama/Meta-Llama-3.1-8B-Instruct

Common Values

ValueCountFrequency (%)
meta-llama/Meta-Llama-3.1-8B-Instruct 621
100.0%

Length

2024-09-28T08:18:43.165945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:43.306516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
meta-llama/meta-llama-3.1-8b-instruct 621
100.0%

Most occurring characters

ValueCountFrequency (%)
a 3726
16.2%
- 3105
13.5%
t 2484
 
10.8%
m 1863
 
8.1%
l 1863
 
8.1%
e 1242
 
5.4%
/ 621
 
2.7%
M 621
 
2.7%
L 621
 
2.7%
3 621
 
2.7%
Other values (10) 6210
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3726
16.2%
- 3105
13.5%
t 2484
 
10.8%
m 1863
 
8.1%
l 1863
 
8.1%
e 1242
 
5.4%
/ 621
 
2.7%
M 621
 
2.7%
L 621
 
2.7%
3 621
 
2.7%
Other values (10) 6210
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3726
16.2%
- 3105
13.5%
t 2484
 
10.8%
m 1863
 
8.1%
l 1863
 
8.1%
e 1242
 
5.4%
/ 621
 
2.7%
M 621
 
2.7%
L 621
 
2.7%
3 621
 
2.7%
Other values (10) 6210
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3726
16.2%
- 3105
13.5%
t 2484
 
10.8%
m 1863
 
8.1%
l 1863
 
8.1%
e 1242
 
5.4%
/ 621
 
2.7%
M 621
 
2.7%
L 621
 
2.7%
3 621
 
2.7%
Other values (10) 6210
27.0%

tensor_parallel_size
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2
621 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters621
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 621
100.0%

Length

2024-09-28T08:18:43.450911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:43.591624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 621
100.0%

Most occurring characters

ValueCountFrequency (%)
2 621
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 621
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 621
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 621
100.0%

pipeline_parallel_size
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1
621 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters621
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 621
100.0%

Length

2024-09-28T08:18:43.748453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:43.884395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 621
100.0%

Most occurring characters

ValueCountFrequency (%)
1 621
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 621
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 621
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 621
100.0%

kv_cache_dtype
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
auto
621 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2484
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowauto
2nd rowauto
3rd rowauto
4th rowauto
5th rowauto

Common Values

ValueCountFrequency (%)
auto 621
100.0%

Length

2024-09-28T08:18:44.030859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:44.162401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
auto 621
100.0%

Most occurring characters

ValueCountFrequency (%)
a 621
25.0%
u 621
25.0%
t 621
25.0%
o 621
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 621
25.0%
u 621
25.0%
t 621
25.0%
o 621
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 621
25.0%
u 621
25.0%
t 621
25.0%
o 621
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 621
25.0%
u 621
25.0%
t 621
25.0%
o 621
25.0%

distributed_backend
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

gpu_memory_utilization
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.9
621 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1863
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.9
2nd row0.9
3rd row0.9
4th row0.9
5th row0.9

Common Values

ValueCountFrequency (%)
0.9 621
100.0%

Length

2024-09-28T08:18:44.310066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:44.451739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.9 621
100.0%

Most occurring characters

ValueCountFrequency (%)
0 621
33.3%
. 621
33.3%
9 621
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 621
33.3%
. 621
33.3%
9 621
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 621
33.3%
. 621
33.3%
9 621
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 621
33.3%
. 621
33.3%
9 621
33.3%

swap_space
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
4
621 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters621
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 621
100.0%

Length

2024-09-28T08:18:44.601706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:44.748106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 621
100.0%

Most occurring characters

ValueCountFrequency (%)
4 621
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 621
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 621
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 621
100.0%

cpu_kv_cache_size
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
40
621 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1242
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row40
4th row40
5th row40

Common Values

ValueCountFrequency (%)
40 621
100.0%

Length

2024-09-28T08:18:44.884741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:45.026207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
40 621
100.0%

Most occurring characters

ValueCountFrequency (%)
4 621
50.0%
0 621
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 621
50.0%
0 621
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 621
50.0%
0 621
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 621
50.0%
0 621
50.0%

block_size
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
8
621 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters621
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 621
100.0%

Length

2024-09-28T08:18:45.162578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:45.317442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
8 621
100.0%

Most occurring characters

ValueCountFrequency (%)
8 621
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 621
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 621
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 621
100.0%

max_num_batched_tokens
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

max_num_seqs
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
128
374 
64
247 

Length

Max length3
Median length3
Mean length2.6022544
Min length2

Characters and Unicode

Total characters1616
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row64
2nd row64
3rd row64
4th row64
5th row64

Common Values

ValueCountFrequency (%)
128 374
60.2%
64 247
39.8%

Length

2024-09-28T08:18:45.472806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:45.614991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
128 374
60.2%
64 247
39.8%

Most occurring characters

ValueCountFrequency (%)
1 374
23.1%
2 374
23.1%
8 374
23.1%
6 247
15.3%
4 247
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 374
23.1%
2 374
23.1%
8 374
23.1%
6 247
15.3%
4 247
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 374
23.1%
2 374
23.1%
8 374
23.1%
6 247
15.3%
4 247
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 374
23.1%
2 374
23.1%
8 374
23.1%
6 247
15.3%
4 247
15.3%

enable_prefix_cache
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size749.0 B
False
621 
ValueCountFrequency (%)
False 621
100.0%
2024-09-28T08:18:45.741432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

enable_chunked_prefill
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

disable_sliding_window
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size749.0 B
False
621 
ValueCountFrequency (%)
False 621
100.0%
2024-09-28T08:18:45.851170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

quantization
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

rope_scaling
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing621
Missing (%)100.0%
Memory size5.0 KiB

enforce_eager
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size749.0 B
False
621 
ValueCountFrequency (%)
False 621
100.0%
2024-09-28T08:18:45.982705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

max_seq_len_to_capture
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
8192
621 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2484
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8192
2nd row8192
3rd row8192
4th row8192
5th row8192

Common Values

ValueCountFrequency (%)
8192 621
100.0%

Length

2024-09-28T08:18:46.136939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:46.267118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
8192 621
100.0%

Most occurring characters

ValueCountFrequency (%)
8 621
25.0%
1 621
25.0%
9 621
25.0%
2 621
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 621
25.0%
1 621
25.0%
9 621
25.0%
2 621
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 621
25.0%
1 621
25.0%
9 621
25.0%
2 621
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 621
25.0%
1 621
25.0%
9 621
25.0%
2 621
25.0%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1
378 
2
243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters621
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 378
60.9%
2 243
39.1%

Length

2024-09-28T08:18:46.412045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:46.561573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 378
60.9%
2 243
39.1%

Most occurring characters

ValueCountFrequency (%)
1 378
60.9%
2 243
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 378
60.9%
2 243
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 378
60.9%
2 243
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 378
60.9%
2 243
39.1%
Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
209 
0.5
208 
1.0
204 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1863
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 209
33.7%
0.5 208
33.5%
1.0 204
32.9%

Length

2024-09-28T08:18:46.720358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:46.881712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 209
33.7%
0.5 208
33.5%
1.0 204
32.9%

Most occurring characters

ValueCountFrequency (%)
0 830
44.6%
. 621
33.3%
5 208
 
11.2%
1 204
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 830
44.6%
. 621
33.3%
5 208
 
11.2%
1 204
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 830
44.6%
. 621
33.3%
5 208
 
11.2%
1 204
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 830
44.6%
. 621
33.3%
5 208
 
11.2%
1 204
 
11.0%

attention_backend
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
TORCH_SDPA
621 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6210
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTORCH_SDPA
2nd rowTORCH_SDPA
3rd rowTORCH_SDPA
4th rowTORCH_SDPA
5th rowTORCH_SDPA

Common Values

ValueCountFrequency (%)
TORCH_SDPA 621
100.0%

Length

2024-09-28T08:18:47.049706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-28T08:18:47.191530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
torch_sdpa 621
100.0%

Most occurring characters

ValueCountFrequency (%)
T 621
10.0%
O 621
10.0%
R 621
10.0%
C 621
10.0%
H 621
10.0%
_ 621
10.0%
S 621
10.0%
D 621
10.0%
P 621
10.0%
A 621
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 621
10.0%
O 621
10.0%
R 621
10.0%
C 621
10.0%
H 621
10.0%
_ 621
10.0%
S 621
10.0%
D 621
10.0%
P 621
10.0%
A 621
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 621
10.0%
O 621
10.0%
R 621
10.0%
C 621
10.0%
H 621
10.0%
_ 621
10.0%
S 621
10.0%
D 621
10.0%
P 621
10.0%
A 621
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 621
10.0%
O 621
10.0%
R 621
10.0%
C 621
10.0%
H 621
10.0%
_ 621
10.0%
S 621
10.0%
D 621
10.0%
P 621
10.0%
A 621
10.0%

Interactions

2024-09-28T08:18:32.111326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:13.663110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.910589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.977175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:18.072283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:20.382474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:22.007905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:23.690129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:25.644536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:27.424776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:29.562793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:32.293966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:13.844170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.987247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:16.155745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:18.258063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:20.551965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:22.136896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:23.834623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:25.769994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:27.610925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:29.753695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:32.458406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.006975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.061375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:16.342437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:18.433363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:20.732137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:22.268086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:23.980785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:25.910480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:27.799993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:29.938859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:32.605342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.190872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.242813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:16.559679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:18.629416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:20.929455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:22.409743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:24.141255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:26.057019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:28.015519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:30.144297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:32.745919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.316865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.317231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:16.724595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:18.796571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:21.116870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:22.534090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:24.280353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:26.196713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:28.191648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:30.333193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:32.873257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.405693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.394307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:16.867519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:18.964670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:21.217518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:22.659534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:24.415222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:26.333493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:28.374923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:30.509131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:33.021946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.491172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.474242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:17.034738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:19.145607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:21.345169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:22.777867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:24.885271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:26.475838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:28.562792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:30.702958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:33.191128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.583201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.563379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:17.249381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:19.644202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:21.484513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:22.975670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:25.038309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:26.645317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:28.766705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:30.912787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:33.352220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.662861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.645370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:17.451060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:19.802336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:21.613195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:23.164505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:25.184597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:26.835087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:28.963560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:31.104590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:33.515308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.744401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.735646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:17.654488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:19.990050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:21.743304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:23.353894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:25.335913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:27.036624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:29.157574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:31.289985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:33.680000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:14.827467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:15.826233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:17.864073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:20.180449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:21.864597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:23.545485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:25.489078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:27.229189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:29.354616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T08:18:31.479093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Missing values

2024-09-28T08:18:34.208172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-28T08:18:35.053519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Engineengine_config_idrun_idModelMean Input TokensMean Output TokensConcurrent RequestsCompleted RequestsDuration (s)Request Throughput (req/min)Output Token Throughput (tok/s)Output Token Throughput per User (tok/s)Mean End to End Latency (s)Mean TTFT (ms)P95 TTFT (ms)Mean Inter Token Latency (ms)P95 Inter Token Latency (ms)attn_input_reshape_minattn_input_reshape_maxattn_input_reshape_meanattn_input_reshape_medianattn_input_reshape_stdattn_kv_cache_save_minattn_kv_cache_save_maxattn_kv_cache_save_meanattn_kv_cache_save_medianattn_kv_cache_save_stdattn_prefill_minattn_prefill_maxattn_prefill_meanattn_prefill_medianattn_prefill_stdattn_decode_minattn_decode_maxattn_decode_meanattn_decode_medianattn_decode_stdattn_output_reshape_minattn_output_reshape_maxattn_output_reshape_meanattn_output_reshape_medianattn_output_reshape_stdembed_minembed_maxembed_meanembed_medianembed_stdinput_layernorm_mininput_layernorm_maxinput_layernorm_meaninput_layernorm_medianinput_layernorm_stdattn_pre_proj_minattn_pre_proj_maxattn_pre_proj_meanattn_pre_proj_medianattn_pre_proj_stdattn_rope_minattn_rope_maxattn_rope_meanattn_rope_medianattn_rope_stdattn_post_proj_minattn_post_proj_maxattn_post_proj_meanattn_post_proj_medianattn_post_proj_stdpost_attention_layernorm_minpost_attention_layernorm_maxpost_attention_layernorm_meanpost_attention_layernorm_medianpost_attention_layernorm_stdmlp_up_proj_minmlp_up_proj_maxmlp_up_proj_meanmlp_up_proj_medianmlp_up_proj_stdmlp_act_minmlp_act_maxmlp_act_meanmlp_act_medianmlp_act_stdmlp_down_proj_minmlp_down_proj_maxmlp_down_proj_meanmlp_down_proj_medianmlp_down_proj_stdmlp_add_minmlp_add_maxmlp_add_meanmlp_add_medianmlp_add_stdmodel_nametensor_parallel_sizepipeline_parallel_sizekv_cache_dtypedistributed_backendgpu_memory_utilizationswap_spacecpu_kv_cache_sizeblock_sizemax_num_batched_tokensmax_num_seqsenable_prefix_cacheenable_chunked_prefilldisable_sliding_windowquantizationrope_scalingenforce_eagermax_seq_len_to_capturenum_scheduler_stepsscheduler_delay_factorattention_backend
0vllmc82767eae67282ebmeta-llama/Meta-Llama-3.1-8B-Instruct50128119.995.7412.2412.829.99134.34134.3478.0278.02NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
1vllmc82767ea7d6bf0cdmeta-llama/Meta-Llama-3.1-8B-Instruct50128101010.7353.50112.4411.7810.70485.02548.8084.8488.25NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
2vllmc82767ead5a06a09meta-llama/Meta-Llama-3.1-8B-Instruct50128202012.5791.85193.1210.0912.50852.09990.9099.16105.46NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
3vllmc82767ead450ced3meta-llama/Meta-Llama-3.1-8B-Instruct50128303013.89125.25260.109.0313.801167.881314.49110.81117.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
4vllmc82767ea935cadebmeta-llama/Meta-Llama-3.1-8B-Instruct50128505017.10170.54354.967.3916.901493.151979.54135.42143.89NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
5vllmc82767eac2ef6f38meta-llama/Meta-Llama-3.1-8B-Instruct50128757529.44148.22309.816.3020.424710.9719092.82163.13243.25NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
6vllmc82767ea88b77240meta-llama/Meta-Llama-3.1-8B-Instruct5012810010033.38175.27368.365.7023.858675.4919821.25188.91265.07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
7vllmc82767eac2f59fc1meta-llama/Meta-Llama-3.1-8B-Instruct502501119.413.0112.1612.4719.41134.31134.3180.2180.21NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
8vllmc82767ea81bc52c5meta-llama/Meta-Llama-3.1-8B-Instruct50250101020.7028.32113.1711.6020.68483.62615.6786.3392.96NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
9vllmc82767ea15ca93a6meta-llama/Meta-Llama-3.1-8B-Instruct50250202023.7749.44194.139.9323.72892.25962.93100.85106.93NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN64FalseNaNFalseNaNNaNFalse819210.0TORCH_SDPA
Engineengine_config_idrun_idModelMean Input TokensMean Output TokensConcurrent RequestsCompleted RequestsDuration (s)Request Throughput (req/min)Output Token Throughput (tok/s)Output Token Throughput per User (tok/s)Mean End to End Latency (s)Mean TTFT (ms)P95 TTFT (ms)Mean Inter Token Latency (ms)P95 Inter Token Latency (ms)attn_input_reshape_minattn_input_reshape_maxattn_input_reshape_meanattn_input_reshape_medianattn_input_reshape_stdattn_kv_cache_save_minattn_kv_cache_save_maxattn_kv_cache_save_meanattn_kv_cache_save_medianattn_kv_cache_save_stdattn_prefill_minattn_prefill_maxattn_prefill_meanattn_prefill_medianattn_prefill_stdattn_decode_minattn_decode_maxattn_decode_meanattn_decode_medianattn_decode_stdattn_output_reshape_minattn_output_reshape_maxattn_output_reshape_meanattn_output_reshape_medianattn_output_reshape_stdembed_minembed_maxembed_meanembed_medianembed_stdinput_layernorm_mininput_layernorm_maxinput_layernorm_meaninput_layernorm_medianinput_layernorm_stdattn_pre_proj_minattn_pre_proj_maxattn_pre_proj_meanattn_pre_proj_medianattn_pre_proj_stdattn_rope_minattn_rope_maxattn_rope_meanattn_rope_medianattn_rope_stdattn_post_proj_minattn_post_proj_maxattn_post_proj_meanattn_post_proj_medianattn_post_proj_stdpost_attention_layernorm_minpost_attention_layernorm_maxpost_attention_layernorm_meanpost_attention_layernorm_medianpost_attention_layernorm_stdmlp_up_proj_minmlp_up_proj_maxmlp_up_proj_meanmlp_up_proj_medianmlp_up_proj_stdmlp_act_minmlp_act_maxmlp_act_meanmlp_act_medianmlp_act_stdmlp_down_proj_minmlp_down_proj_maxmlp_down_proj_meanmlp_down_proj_medianmlp_down_proj_stdmlp_add_minmlp_add_maxmlp_add_meanmlp_add_medianmlp_add_stdmodel_nametensor_parallel_sizepipeline_parallel_sizekv_cache_dtypedistributed_backendgpu_memory_utilizationswap_spacecpu_kv_cache_sizeblock_sizemax_num_batched_tokensmax_num_seqsenable_prefix_cacheenable_chunked_prefilldisable_sliding_windowquantizationrope_scalingenforce_eagermax_seq_len_to_capturenum_scheduler_stepsscheduler_delay_factorattention_backend
611vllm4e74f945c95f84f5meta-llama/Meta-Llama-3.1-8B-Instruct5502501139.511.506.286.3539.51398.51398.51157.40157.40NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN128FalseNaNFalseNaNNaNFalse819221.0TORCH_SDPA
612vllm4e74f945bbfb7a15meta-llama/Meta-Llama-3.1-8B-Instruct550250101047.0112.6452.835.3546.922350.333087.98187.09187.71NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN128FalseNaNFalseNaNNaNFalse819221.0TORCH_SDPA
613vllm4e74f945e19902bemeta-llama/Meta-Llama-3.1-8B-Instruct550250202059.1120.1584.264.2559.054613.305685.80235.31235.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmeta-llama/Meta-Llama-3.1-8B-Instruct21autoNaN0.94408NaN128FalseNaNFalseNaNNaNFalse819221.0TORCH_SDPA
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